Plumbing for measures: algorithms that quantify properties of datasets.

Besides the Measure base class this module also provides the (abstract) FeaturewiseMeasure class. The difference between a general measure and the output of the FeaturewiseMeasure is that the latter returns a 1d map (one value per feature in the dataset). In contrast there are no restrictions on the returned value of Measure except for that it has to be in some iterable container.

Inheritance diagram of mvpa2.measures.base



BinaryClassifierSensitivityAnalyzer(*args_, ...) Set sensitivity analyzer output to have proper labels
BoostedClassifierSensitivityAnalyzer(*args_, ...) Set sensitivity analyzers to be merged into a single output
CombinedFeaturewiseMeasure([analyzers, sa_attr]) Set sensitivity analyzers to be merged into a single output
CrossValidation(learner, generator[, ...]) Cross-validate a learner’s transfer on datasets.


FeaturewiseMeasure([null_dist]) A per-feature-measure computed from a Dataset (base class).
MappedClassifierSensitivityAnalyzer(*args_, ...) Set sensitivity analyzer output be reverse mapped using mapper of the
Measure([null_dist]) A measure computed from a Dataset
ProxyClassifierSensitivityAnalyzer(*args_, ...) Set sensitivity analyzer output just to pass through
ProxyMeasure(measure[, skip_train]) Wrapper to allow for alternative post-processing of a shared measure.
RegressionAsClassifierSensitivityAnalyzer(...) Set sensitivity analyzer output to have proper labels
RepeatedMeasure(node, generator[, callback, ...]) Repeatedly run a measure on generated dataset.
Sensitivity(clf[, force_train]) Sensitivities of features for a given Classifier.
StaticMeasure([measure, bias]) A static (assigned) sensitivity measure.
TransferMeasure(measure, splitter, **kwargs) Train and run a measure on two different parts of a dataset.